A composite likelihood approach to latent multivariate Gaussian modeling of SNP data with application to genetic association testing.

ABSTRACT: Many statistical tests have been proposed for case-control data to detect disease association with multiple single nucleotide polymorphisms (SNPs) in linkage disequilibrium. The main reason for the existence of so many tests is that each test aims to detect one or two aspects of many possible distributional differences between cases and controls, largely due to the lack of a general and yet simple model for discrete genotype data. Here we propose a latent variable model to represent SNP data: the observed SNP data are assumed to be obtained by discretizing a latent multivariate Gaussian variate. Because the latent variate is multivariate Gaussian, its distribution is completely characterized by its mean vector and covariance matrix, in contrast to much more complex forms of a general distr

SUBMITTER: Han F 

PROVIDER: S-EPMC3218301 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature